def testPolicy(self, symbol = "IBM", \
sd=dt.datetime(2009,1,1), \
ed=dt.datetime(2010,1,1), \
sv = 10000):
current_holding = 0
dates = pd.date_range(sd, ed)
prices_all = ut.get_data([symbol], dates)
trades = prices_all[[
symbol,
]].copy(deep=True)
trades_SPY = prices_all['SPY']
window_size = self.window_size
idt_size = self.idt_size
#check indicators
#SMA,BB,MM = idt.calculate_indicators(symbols=[symbol],sd=sd,ed=ed,window_size=self.window_size,plot_fig=False)
#check indicators
prices = ut.get_data([symbol], pd.date_range(sd, ed))
prices = prices[symbol]
SMA = manu.simple_moving_average(prices, window_size=20)
# sma_int = prices[symbol]/SMA['SMA']-1
# BB = manu.bollinger_band(prices,window_size=20)
# MM = manu.momentum(prices,window_size = 20)
trades.values[:, :] = 0
Xtest = []
# prices = prices_all[symbol]
for i in range(window_size + idt_size + 1, len(prices) - 1):
data = np.array(SMA[i - idt_size:i])
#data = np.array(SMA[i-idt_size:i])
Xtest.append(data)
result = self.learner.query(Xtest)
for i, r in enumerate(result):
if r > 0:
trades.values[i + window_size + idt_size +
1, :] = 1000 - current_holding
current_holding = 1000
elif r < 0:
trades.values[i + window_size + idt_size +
1, :] = -1000 - current_holding
current_holding = -1000
# if self.verbose: print type(trades)
# if self.verbose: print trades
# if self.verbose: print prices_all
return trades
def addEvidence(self, symbol = "IBM", \
sd=dt.datetime(2008,1,1), \
ed=dt.datetime(2009,1,1), \
sv = 10000):
window_size = self.window_size
idt_size = self.idt_size
#Check for N day return
N = self.N
threshold = max(0.03, 2 * self.impact)
#check indicators
prices = ut.get_data([symbol], pd.date_range(sd, ed))
prices = prices[symbol]
SMA = manu.simple_moving_average(prices, window_size=20)
# sma_int = prices[symbol]/SMA['SMA']-1
# BB = manu.bollinger_band(prices,window_size=20)
# MM = manu.momentum(prices,window_size = 20)
# MM = idt.calculate_indicators(symbols=[symbol],sd=sd,ed=ed,window_size=self.window_size,plot_fig=False)
#turn regression model to Classification
X = []
Y = []
for i in range(window_size + idt_size + 1, len(prices) - N):
X.append(np.array(SMA[i - idt_size:i]))
# X.append(np.array(SMA[i-idt_size:i]))
gain = (prices.values[i + N] - prices.values[i]) / prices.values[i]
if gain > threshold:
Y.append(1)
elif gain < -threshold:
Y.append(-1)
else:
Y.append(0)
X = np.array(X)
Y = np.array(Y)
self.learner.addEvidence(X, Y)